LAUSR

ABSTRACT Quality improvement in micro-manufacturing processes relies on empirical models. However, if an estimated model varies from the true model because of random errors in experiments, the resulting operating conditions may be located far from the true optimal operating conditions. Using the Pareto chart, which highlights the most important among a set of factors, this article develops a novel ensemble modelling technique which considers the model selection via bootstrap methods. In addition, an integrative optimization strategy is proposed based on interval-data theory, in which the squared bias and the reliability of operating conditions are incorporated into a single framework of a strategy. The proposed method is illustrated with a micro-drilling process, which clearly shows how useful and effective the proposed method is. Through comparative studies, it is also shown that the proposed method has a good robustness property and it provides a reliable scheme for optimizing the machining parameters.